Overview

Dataset statistics

Number of variables18
Number of observations239194
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.5 MiB
Average record size in memory138.0 B

Variable types

Numeric9
Boolean1
Categorical7
Unsupported1

Alerts

visible has constant value "True" Constant
sensor-type has constant value "gps" Constant
individual-taxon-canonical-name has constant value "Canis lupus" Constant
study-name has constant value "ABoVE: Boutin Alberta Grey Wolf" Constant
timestamp has a high cardinality: 189637 distinct values High cardinality
study-local-timestamp has a high cardinality: 189637 distinct values High cardinality
location-long is highly correlated with height-raw and 1 other fieldsHigh correlation
location-lat is highly correlated with utm-northingHigh correlation
height-raw is highly correlated with location-long and 1 other fieldsHigh correlation
utm-easting is highly correlated with location-long and 1 other fieldsHigh correlation
utm-northing is highly correlated with location-latHigh correlation
location-long is highly correlated with height-raw and 1 other fieldsHigh correlation
location-lat is highly correlated with height-raw and 1 other fieldsHigh correlation
height-raw is highly correlated with location-long and 3 other fieldsHigh correlation
utm-easting is highly correlated with location-long and 1 other fieldsHigh correlation
utm-northing is highly correlated with location-lat and 1 other fieldsHigh correlation
location-long is highly correlated with utm-eastingHigh correlation
location-lat is highly correlated with utm-northingHigh correlation
utm-easting is highly correlated with location-longHigh correlation
utm-northing is highly correlated with location-latHigh correlation
individual-taxon-canonical-name is highly correlated with visible and 4 other fieldsHigh correlation
visible is highly correlated with individual-taxon-canonical-name and 4 other fieldsHigh correlation
study-name is highly correlated with individual-taxon-canonical-name and 4 other fieldsHigh correlation
sensor-type is highly correlated with individual-taxon-canonical-name and 4 other fieldsHigh correlation
study-timezone is highly correlated with individual-taxon-canonical-name and 3 other fieldsHigh correlation
utm-zone is highly correlated with individual-taxon-canonical-name and 3 other fieldsHigh correlation
event-id is highly correlated with location-long and 3 other fieldsHigh correlation
location-long is highly correlated with event-id and 4 other fieldsHigh correlation
location-lat is highly correlated with event-id and 4 other fieldsHigh correlation
external-temperature is highly correlated with study-timezoneHigh correlation
tag-local-identifier is highly correlated with event-idHigh correlation
utm-easting is highly correlated with location-long and 3 other fieldsHigh correlation
utm-northing is highly correlated with event-id and 4 other fieldsHigh correlation
utm-zone is highly correlated with location-long and 3 other fieldsHigh correlation
study-timezone is highly correlated with external-temperatureHigh correlation
event-id is uniformly distributed Uniform
timestamp is uniformly distributed Uniform
study-local-timestamp is uniformly distributed Uniform
event-id has unique values Unique
individual-local-identifier is an unsupported type, check if it needs cleaning or further analysis Unsupported
external-temperature has 7883 (3.3%) zeros Zeros

Reproduction

Analysis started2021-11-11 22:19:58.138942
Analysis finished2021-11-11 22:20:32.226047
Duration34.09 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

event-id
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct239194
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9710414520
Minimum9710294924
Maximum9710534117
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-11-11T17:20:32.326289image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum9710294924
5-th percentile9710306884
Q19710354722
median9710414520
Q39710474319
95-th percentile9710522157
Maximum9710534117
Range239193
Interquartile range (IQR)119596.5

Descriptive statistics

Standard deviation69049.50448
Coefficient of variation (CV)7.110870945 × 10-6
Kurtosis-1.2
Mean9710414520
Median Absolute Deviation (MAD)59798.5
Skewness4.498026642 × 10-16
Sum2.322672891 × 1015
Variance4767834069
MonotonicityNot monotonic
2021-11-11T17:20:32.460900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97103400971
 
< 0.1%
97105175011
 
< 0.1%
97105256811
 
< 0.1%
97105236321
 
< 0.1%
97105215871
 
< 0.1%
97105195381
 
< 0.1%
97105338771
 
< 0.1%
97105318281
 
< 0.1%
97105297831
 
< 0.1%
97105277341
 
< 0.1%
Other values (239184)239184
> 99.9%
ValueCountFrequency (%)
97102949241
< 0.1%
97102949251
< 0.1%
97102949261
< 0.1%
97102949271
< 0.1%
97102949281
< 0.1%
97102949291
< 0.1%
97102949301
< 0.1%
97102949311
< 0.1%
97102949321
< 0.1%
97102949331
< 0.1%
ValueCountFrequency (%)
97105341171
< 0.1%
97105341161
< 0.1%
97105341151
< 0.1%
97105341141
< 0.1%
97105341131
< 0.1%
97105341121
< 0.1%
97105341111
< 0.1%
97105341101
< 0.1%
97105341091
< 0.1%
97105341081
< 0.1%

visible
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size467.3 KiB
True
239194 
ValueCountFrequency (%)
True239194
100.0%
2021-11-11T17:20:32.549749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

timestamp
Categorical

HIGH CARDINALITY
UNIFORM

Distinct189637
Distinct (%)79.3%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2012-03-23 10:00:29.000
 
16
2012-03-27 10:00:29.000
 
15
2012-03-21 10:00:29.000
 
15
2012-03-27 22:00:29.000
 
14
2012-04-04 22:00:29.000
 
14
Other values (189632)
239120 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters5501462
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique154667 ?
Unique (%)64.7%

Sample

1st row2013-12-19 00:00:44.000
2nd row2013-12-19 12:00:45.000
3rd row2013-12-20 00:00:44.000
4th row2013-12-20 12:00:44.000
5th row2013-12-21 00:00:44.000

Common Values

ValueCountFrequency (%)
2012-03-23 10:00:29.00016
 
< 0.1%
2012-03-27 10:00:29.00015
 
< 0.1%
2012-03-21 10:00:29.00015
 
< 0.1%
2012-03-27 22:00:29.00014
 
< 0.1%
2012-04-04 22:00:29.00014
 
< 0.1%
2012-03-25 04:00:29.00014
 
< 0.1%
2012-03-25 22:00:29.00013
 
< 0.1%
2012-03-21 16:00:29.00013
 
< 0.1%
2012-04-07 16:00:29.00013
 
< 0.1%
2012-04-05 10:00:29.00013
 
< 0.1%
Other values (189627)239054
99.9%

Length

2021-11-11T17:20:32.612690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10:00:29.0002109
 
0.4%
04:00:29.0001941
 
0.4%
22:00:29.0001900
 
0.4%
07:00:29.0001882
 
0.4%
16:00:29.0001828
 
0.4%
2012-04-101824
 
0.4%
2012-04-121822
 
0.4%
2012-04-131821
 
0.4%
2012-04-161821
 
0.4%
2012-04-091820
 
0.4%
Other values (14704)459620
96.1%

Most occurring characters

ValueCountFrequency (%)
01726526
31.4%
1784017
14.3%
2599797
 
10.9%
-478388
 
8.7%
:478388
 
8.7%
3261920
 
4.8%
4239396
 
4.4%
239194
 
4.3%
.239194
 
4.3%
5136034
 
2.5%
Other values (4)318608
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4066298
73.9%
Other Punctuation717582
 
13.0%
Dash Punctuation478388
 
8.7%
Space Separator239194
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01726526
42.5%
1784017
19.3%
2599797
 
14.8%
3261920
 
6.4%
4239396
 
5.9%
5136034
 
3.3%
695287
 
2.3%
987433
 
2.2%
768587
 
1.7%
867301
 
1.7%
Other Punctuation
ValueCountFrequency (%)
:478388
66.7%
.239194
33.3%
Dash Punctuation
ValueCountFrequency (%)
-478388
100.0%
Space Separator
ValueCountFrequency (%)
239194
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5501462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01726526
31.4%
1784017
14.3%
2599797
 
10.9%
-478388
 
8.7%
:478388
 
8.7%
3261920
 
4.8%
4239396
 
4.4%
239194
 
4.3%
.239194
 
4.3%
5136034
 
2.5%
Other values (4)318608
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII5501462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01726526
31.4%
1784017
14.3%
2599797
 
10.9%
-478388
 
8.7%
:478388
 
8.7%
3261920
 
4.8%
4239396
 
4.4%
239194
 
4.3%
.239194
 
4.3%
5136034
 
2.5%
Other values (4)318608
 
5.8%

location-long
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct183369
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-111.7361772
Minimum-115.791907
Maximum-110.703109
Zeros0
Zeros (%)0.0%
Negative239194
Negative (%)100.0%
Memory size1.8 MiB
2021-11-11T17:20:32.718669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-115.791907
5-th percentile-112.3091405
Q1-111.9618555
median-111.671905
Q3-111.477346
95-th percentile-111.2075907
Maximum-110.703109
Range5.088798
Interquartile range (IQR)0.4845095

Descriptive statistics

Standard deviation0.3654941019
Coefficient of variation (CV)-0.003271045342
Kurtosis2.340819931
Mean-111.7361772
Median Absolute Deviation (MAD)0.233387
Skewness-0.9266874694
Sum-26726623.16
Variance0.1335859386
MonotonicityNot monotonic
2021-11-11T17:20:32.855905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-111.93284641
 
< 0.1%
-111.93287638
 
< 0.1%
-111.93121132
 
< 0.1%
-111.29628126
 
< 0.1%
-111.43085225
 
< 0.1%
-112.07790525
 
< 0.1%
-111.93122425
 
< 0.1%
-111.45025823
 
< 0.1%
-111.4502722
 
< 0.1%
-111.93288921
 
< 0.1%
Other values (183359)238916
99.9%
ValueCountFrequency (%)
-115.7919071
< 0.1%
-115.7549281
< 0.1%
-115.7419211
< 0.1%
-115.7387461
< 0.1%
-115.7016421
< 0.1%
-115.6285521
< 0.1%
-115.6039471
< 0.1%
-115.6038061
< 0.1%
-115.6016281
< 0.1%
-115.5992291
< 0.1%
ValueCountFrequency (%)
-110.7031091
< 0.1%
-110.7865281
< 0.1%
-110.8453711
< 0.1%
-110.8454211
< 0.1%
-110.8461511
< 0.1%
-110.8725211
< 0.1%
-110.8785951
< 0.1%
-110.8787311
< 0.1%
-110.8849231
< 0.1%
-110.8887841
< 0.1%

location-lat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct184497
Distinct (%)77.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.09680406
Minimum55.622621
Maximum60.568555
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-11-11T17:20:32.986456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum55.622621
5-th percentile56.56211465
Q156.7058
median56.9332795
Q357.34693125
95-th percentile57.73891035
Maximum60.568555
Range4.945934
Interquartile range (IQR)0.64113125

Descriptive statistics

Standard deviation0.6912884859
Coefficient of variation (CV)0.01210730613
Kurtosis9.55094609
Mean57.09680406
Median Absolute Deviation (MAD)0.2572585
Skewness2.786582195
Sum13657212.95
Variance0.4778797708
MonotonicityNot monotonic
2021-11-11T17:20:33.120501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56.68395620
 
< 0.1%
57.07231818
 
< 0.1%
57.07231318
 
< 0.1%
57.07232816
 
< 0.1%
57.07232715
 
< 0.1%
57.07230315
 
< 0.1%
57.07237915
 
< 0.1%
57.07236915
 
< 0.1%
56.96030314
 
< 0.1%
57.07238313
 
< 0.1%
Other values (184487)239035
99.9%
ValueCountFrequency (%)
55.6226211
< 0.1%
55.6251931
< 0.1%
55.6256051
< 0.1%
55.6282261
< 0.1%
55.6287231
< 0.1%
55.6287281
< 0.1%
55.6287451
< 0.1%
55.6287461
< 0.1%
55.6287591
< 0.1%
55.6287711
< 0.1%
ValueCountFrequency (%)
60.5685551
< 0.1%
60.5685081
< 0.1%
60.5684961
< 0.1%
60.5684851
< 0.1%
60.5466091
< 0.1%
60.4982921
< 0.1%
60.4521331
< 0.1%
60.4065671
< 0.1%
60.4064891
< 0.1%
60.3677341
< 0.1%

external-temperature
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct86
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.715912607
Minimum-33
Maximum55
Zeros7883
Zeros (%)3.3%
Negative81436
Negative (%)34.0%
Memory size1.8 MiB
2021-11-11T17:20:33.254001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-33
5-th percentile-15
Q1-3
median4
Q314
95-th percentile24
Maximum55
Range88
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.90581803
Coefficient of variation (CV)2.524605316
Kurtosis-0.3340920936
Mean4.715912607
Median Absolute Deviation (MAD)8
Skewness0.02194794282
Sum1128018
Variance141.7485031
MonotonicityNot monotonic
2021-11-11T17:20:33.375058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-18397
 
3.5%
28378
 
3.5%
18336
 
3.5%
48059
 
3.4%
38020
 
3.4%
07883
 
3.3%
-27880
 
3.3%
-37835
 
3.3%
67509
 
3.1%
57506
 
3.1%
Other values (76)159391
66.6%
ValueCountFrequency (%)
-3311
 
< 0.1%
-3221
 
< 0.1%
-3025
 
< 0.1%
-2966
 
< 0.1%
-28116
 
< 0.1%
-27191
 
0.1%
-26289
0.1%
-25386
0.2%
-24473
0.2%
-23649
0.3%
ValueCountFrequency (%)
553
 
< 0.1%
542
 
< 0.1%
534
 
< 0.1%
522
 
< 0.1%
513
 
< 0.1%
502
 
< 0.1%
494
 
< 0.1%
483
 
< 0.1%
472
 
< 0.1%
4612
< 0.1%

gps:dop
Real number (ℝ≥0)

Distinct103
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.436039366
Minimum0.2
Maximum24.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-11-11T17:20:33.498704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.6
Q11.8
median2
Q32.8
95-th percentile5
Maximum24.8
Range24.6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.528486256
Coefficient of variation (CV)0.6274472727
Kurtosis36.99050467
Mean2.436039366
Median Absolute Deviation (MAD)0.2
Skewness4.469849627
Sum582686
Variance2.336270235
MonotonicityNot monotonic
2021-11-11T17:20:33.625863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.844157
18.5%
241587
17.4%
1.641173
17.2%
2.234925
14.6%
3.213117
 
5.5%
3.410569
 
4.4%
0.610299
 
4.3%
3.68744
 
3.7%
3.87236
 
3.0%
4.83213
 
1.3%
Other values (93)24174
10.1%
ValueCountFrequency (%)
0.238
 
< 0.1%
0.42028
 
0.8%
0.610299
 
4.3%
13
 
< 0.1%
1.2161
 
0.1%
1.4871
 
0.4%
1.641173
17.2%
1.844157
18.5%
241587
17.4%
2.234925
14.6%
ValueCountFrequency (%)
24.81
 
< 0.1%
24.66
 
< 0.1%
24.46
 
< 0.1%
24.23
 
< 0.1%
2428
< 0.1%
23.21
 
< 0.1%
2310
 
< 0.1%
22.87
 
< 0.1%
22.610
 
< 0.1%
22.49
 
< 0.1%

height-raw
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct38907
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean343.5655176
Minimum-83.62
Maximum6337.09
Zeros0
Zeros (%)0.0%
Negative8
Negative (%)< 0.1%
Memory size1.8 MiB
2021-11-11T17:20:33.885512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-83.62
5-th percentile205.28
Q1282.05
median338.95
Q3401.95
95-th percentile488.54
Maximum6337.09
Range6420.71
Interquartile range (IQR)119.9

Descriptive statistics

Standard deviation87.31794086
Coefficient of variation (CV)0.2541522254
Kurtosis93.76256085
Mean343.5655176
Median Absolute Deviation (MAD)59.83
Skewness1.627299106
Sum82178810.41
Variance7624.422796
MonotonicityNot monotonic
2021-11-11T17:20:34.006999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
386.3834
 
< 0.1%
384.730
 
< 0.1%
321.7727
 
< 0.1%
348.3327
 
< 0.1%
309.0427
 
< 0.1%
322.2325
 
< 0.1%
322.5725
 
< 0.1%
342.8125
 
< 0.1%
328.825
 
< 0.1%
329.6525
 
< 0.1%
Other values (38897)238924
99.9%
ValueCountFrequency (%)
-83.621
< 0.1%
-73.711
< 0.1%
-65.51
< 0.1%
-51.431
< 0.1%
-33.461
< 0.1%
-13.781
< 0.1%
-12.91
< 0.1%
-4.51
< 0.1%
0.431
< 0.1%
2.821
< 0.1%
ValueCountFrequency (%)
6337.091
< 0.1%
2254.371
< 0.1%
1645.031
< 0.1%
1300.341
< 0.1%
1075.571
< 0.1%
1007.781
< 0.1%
1007.741
< 0.1%
924.641
< 0.1%
921.791
< 0.1%
914.031
< 0.1%

sensor-type
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
gps
239194 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters717582
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgps
2nd rowgps
3rd rowgps
4th rowgps
5th rowgps

Common Values

ValueCountFrequency (%)
gps239194
100.0%

Length

2021-11-11T17:20:34.128619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-11T17:20:34.195278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
gps239194
100.0%

Most occurring characters

ValueCountFrequency (%)
g239194
33.3%
p239194
33.3%
s239194
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter717582
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
g239194
33.3%
p239194
33.3%
s239194
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin717582
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
g239194
33.3%
p239194
33.3%
s239194
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII717582
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
g239194
33.3%
p239194
33.3%
s239194
33.3%

individual-taxon-canonical-name
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Canis lupus
239194 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters2631134
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCanis lupus
2nd rowCanis lupus
3rd rowCanis lupus
4th rowCanis lupus
5th rowCanis lupus

Common Values

ValueCountFrequency (%)
Canis lupus239194
100.0%

Length

2021-11-11T17:20:34.253715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-11T17:20:34.314185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
canis239194
50.0%
lupus239194
50.0%

Most occurring characters

ValueCountFrequency (%)
s478388
18.2%
u478388
18.2%
C239194
9.1%
a239194
9.1%
n239194
9.1%
i239194
9.1%
239194
9.1%
l239194
9.1%
p239194
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2152746
81.8%
Uppercase Letter239194
 
9.1%
Space Separator239194
 
9.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s478388
22.2%
u478388
22.2%
a239194
11.1%
n239194
11.1%
i239194
11.1%
l239194
11.1%
p239194
11.1%
Uppercase Letter
ValueCountFrequency (%)
C239194
100.0%
Space Separator
ValueCountFrequency (%)
239194
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2391940
90.9%
Common239194
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
s478388
20.0%
u478388
20.0%
C239194
10.0%
a239194
10.0%
n239194
10.0%
i239194
10.0%
l239194
10.0%
p239194
10.0%
Common
ValueCountFrequency (%)
239194
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2631134
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s478388
18.2%
u478388
18.2%
C239194
9.1%
a239194
9.1%
n239194
9.1%
i239194
9.1%
239194
9.1%
l239194
9.1%
p239194
9.1%

tag-local-identifier
Real number (ℝ≥0)

HIGH CORRELATION

Distinct43
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31031.32844
Minimum13790
Maximum35260
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-11-11T17:20:34.385463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum13790
5-th percentile13794
Q132255
median32263
Q333672
95-th percentile33679
Maximum35260
Range21470
Interquartile range (IQR)1417

Descriptive statistics

Standard deviation5537.370128
Coefficient of variation (CV)0.1784445078
Kurtosis5.686306793
Mean31031.32844
Median Absolute Deviation (MAD)11
Skewness-2.742033107
Sum7422507575
Variance30662467.94
MonotonicityNot monotonic
2021-11-11T17:20:34.499770image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3226116414
 
6.9%
3226312896
 
5.4%
3367811457
 
4.8%
3225411391
 
4.8%
1379411356
 
4.7%
3225111344
 
4.7%
3367910913
 
4.6%
322648827
 
3.7%
322528709
 
3.6%
322598703
 
3.6%
Other values (33)127184
53.2%
ValueCountFrequency (%)
137905531
2.3%
13791661
 
0.3%
137923383
 
1.4%
137931032
 
0.4%
1379411356
4.7%
1500839
 
< 0.1%
15009105
 
< 0.1%
3225111344
4.7%
322528709
3.6%
322533949
 
1.7%
ValueCountFrequency (%)
3526085
 
< 0.1%
336815206
2.2%
336804716
2.0%
3367910913
4.6%
3367811457
4.8%
336776252
2.6%
336764316
 
1.8%
336753816
 
1.6%
336747789
3.3%
336733834
 
1.6%

individual-local-identifier
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size1.8 MiB

study-name
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
ABoVE: Boutin Alberta Grey Wolf
239194 

Length

Max length31
Median length31
Mean length31
Min length31

Characters and Unicode

Total characters7415014
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowABoVE: Boutin Alberta Grey Wolf
2nd rowABoVE: Boutin Alberta Grey Wolf
3rd rowABoVE: Boutin Alberta Grey Wolf
4th rowABoVE: Boutin Alberta Grey Wolf
5th rowABoVE: Boutin Alberta Grey Wolf

Common Values

ValueCountFrequency (%)
ABoVE: Boutin Alberta Grey Wolf239194
100.0%

Length

2021-11-11T17:20:34.607643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-11T17:20:34.669973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
above239194
20.0%
boutin239194
20.0%
alberta239194
20.0%
grey239194
20.0%
wolf239194
20.0%

Most occurring characters

ValueCountFrequency (%)
956776
 
12.9%
o717582
 
9.7%
A478388
 
6.5%
e478388
 
6.5%
t478388
 
6.5%
r478388
 
6.5%
B478388
 
6.5%
l478388
 
6.5%
W239194
 
3.2%
y239194
 
3.2%
Other values (10)2391940
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4305492
58.1%
Uppercase Letter1913552
25.8%
Space Separator956776
 
12.9%
Other Punctuation239194
 
3.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o717582
16.7%
e478388
11.1%
t478388
11.1%
r478388
11.1%
l478388
11.1%
y239194
 
5.6%
a239194
 
5.6%
n239194
 
5.6%
b239194
 
5.6%
i239194
 
5.6%
Other values (2)478388
11.1%
Uppercase Letter
ValueCountFrequency (%)
A478388
25.0%
B478388
25.0%
W239194
12.5%
G239194
12.5%
E239194
12.5%
V239194
12.5%
Space Separator
ValueCountFrequency (%)
956776
100.0%
Other Punctuation
ValueCountFrequency (%)
:239194
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6219044
83.9%
Common1195970
 
16.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o717582
 
11.5%
A478388
 
7.7%
e478388
 
7.7%
t478388
 
7.7%
r478388
 
7.7%
B478388
 
7.7%
l478388
 
7.7%
W239194
 
3.8%
y239194
 
3.8%
G239194
 
3.8%
Other values (8)1913552
30.8%
Common
ValueCountFrequency (%)
956776
80.0%
:239194
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII7415014
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
956776
 
12.9%
o717582
 
9.7%
A478388
 
6.5%
e478388
 
6.5%
t478388
 
6.5%
r478388
 
6.5%
B478388
 
6.5%
l478388
 
6.5%
W239194
 
3.2%
y239194
 
3.2%
Other values (10)2391940
32.3%

utm-easting
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct233158
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean455378.6155
Minimum321264.6663
Maximum666940.885
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-11-11T17:20:34.742166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum321264.6663
5-th percentile420062.0664
Q1441570.828
median459903.0252
Q3471141.1709
95-th percentile487318.566
Maximum666940.885
Range345676.2187
Interquartile range (IQR)29570.34288

Descriptive statistics

Standard deviation22444.5914
Coefficient of variation (CV)0.04928775889
Kurtosis1.43709283
Mean455378.6155
Median Absolute Deviation (MAD)14085.83387
Skewness-0.8460651585
Sum1.089238326 × 1011
Variance503759683.3
MonotonicityNot monotonic
2021-11-11T17:20:34.865522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
443440.747316
 
< 0.1%
443442.573714
 
< 0.1%
481855.428512
 
< 0.1%
443541.775912
 
< 0.1%
443442.588912
 
< 0.1%
473798.894110
 
< 0.1%
477807.173810
 
< 0.1%
443440.76110
 
< 0.1%
443541.00910
 
< 0.1%
443541.79110
 
< 0.1%
Other values (233148)239078
> 99.9%
ValueCountFrequency (%)
321264.66631
< 0.1%
332765.23641
< 0.1%
332962.48681
< 0.1%
333001.03381
< 0.1%
337731.12751
< 0.1%
340792.77341
< 0.1%
343707.63441
< 0.1%
345914.7911
< 0.1%
345915.17541
< 0.1%
345915.2991
< 0.1%
ValueCountFrequency (%)
666940.8851
< 0.1%
638158.09091
< 0.1%
617525.60781
< 0.1%
616721.39551
< 0.1%
616287.53231
< 0.1%
615732.67731
< 0.1%
608440.00621
< 0.1%
600237.49661
< 0.1%
599977.52091
< 0.1%
599922.24811
< 0.1%

utm-northing
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct233158
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6328465.624
Minimum6165653.236
Maximum6715338.568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-11-11T17:20:34.999170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6165653.236
5-th percentile6269219.647
Q16284742.306
median6310361.042
Q36356189.124
95-th percentile6399735.457
Maximum6715338.568
Range549685.332
Interquartile range (IQR)71446.81812

Descriptive statistics

Standard deviation76887.2272
Coefficient of variation (CV)0.01214942638
Kurtosis9.575902845
Mean6328465.624
Median Absolute Deviation (MAD)28796.21584
Skewness2.794177083
Sum1.513731006 × 1012
Variance5911645706
MonotonicityNot monotonic
2021-11-11T17:20:35.123363image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6325822.00216
 
< 0.1%
6325822.53414
 
< 0.1%
6325823.64712
 
< 0.1%
6283664.4612
 
< 0.1%
6325826.85712
 
< 0.1%
6296819.19810
 
< 0.1%
6325827.9710
 
< 0.1%
6313049.63410
 
< 0.1%
6325823.00410
 
< 0.1%
6325828.42610
 
< 0.1%
Other values (233148)239078
> 99.9%
ValueCountFrequency (%)
6165653.2361
< 0.1%
6165961.0441
< 0.1%
6165964.2541
< 0.1%
6166253.3241
< 0.1%
6166367.6541
< 0.1%
6166367.9871
< 0.1%
6166369.9841
< 0.1%
6166369.9961
< 0.1%
6166371.5451
< 0.1%
6166372.8431
< 0.1%
ValueCountFrequency (%)
6715338.5681
< 0.1%
6715333.3511
< 0.1%
6715332.0531
< 0.1%
6715330.6961
< 0.1%
6712897.6981
< 0.1%
6707454.5391
< 0.1%
6702241.9411
< 0.1%
6697143.3651
< 0.1%
6697133.7811
< 0.1%
6692797.7091
< 0.1%

utm-zone
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
12N
239161 
11N
 
33

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters717582
Distinct characters3
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12N
2nd row12N
3rd row12N
4th row12N
5th row12N

Common Values

ValueCountFrequency (%)
12N239161
> 99.9%
11N33
 
< 0.1%

Length

2021-11-11T17:20:35.247125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-11T17:20:35.309219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
12n239161
> 99.9%
11n33
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1239227
33.3%
N239194
33.3%
2239161
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number478388
66.7%
Uppercase Letter239194
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1239227
50.0%
2239161
50.0%
Uppercase Letter
ValueCountFrequency (%)
N239194
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common478388
66.7%
Latin239194
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1239227
50.0%
2239161
50.0%
Latin
ValueCountFrequency (%)
N239194
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII717582
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1239227
33.3%
N239194
33.3%
2239161
33.3%

study-timezone
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Mountain Daylight Time
127047 
Mountain Standard Time
112147 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters5262268
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMountain Standard Time
2nd rowMountain Standard Time
3rd rowMountain Standard Time
4th rowMountain Standard Time
5th rowMountain Standard Time

Common Values

ValueCountFrequency (%)
Mountain Daylight Time127047
53.1%
Mountain Standard Time112147
46.9%

Length

2021-11-11T17:20:35.375455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-11T17:20:35.438357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
mountain239194
33.3%
time239194
33.3%
daylight127047
17.7%
standard112147
15.6%

Most occurring characters

ValueCountFrequency (%)
i605435
11.5%
n590535
11.2%
a590535
11.2%
t478388
 
9.1%
478388
 
9.1%
M239194
 
4.5%
T239194
 
4.5%
u239194
 
4.5%
o239194
 
4.5%
e239194
 
4.5%
Other values (9)1323017
25.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4066298
77.3%
Uppercase Letter717582
 
13.6%
Space Separator478388
 
9.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i605435
14.9%
n590535
14.5%
a590535
14.5%
t478388
11.8%
u239194
 
5.9%
o239194
 
5.9%
e239194
 
5.9%
m239194
 
5.9%
d224294
 
5.5%
y127047
 
3.1%
Other values (4)493288
12.1%
Uppercase Letter
ValueCountFrequency (%)
M239194
33.3%
T239194
33.3%
D127047
17.7%
S112147
15.6%
Space Separator
ValueCountFrequency (%)
478388
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4783880
90.9%
Common478388
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i605435
12.7%
n590535
12.3%
a590535
12.3%
t478388
10.0%
M239194
 
5.0%
T239194
 
5.0%
u239194
 
5.0%
o239194
 
5.0%
e239194
 
5.0%
m239194
 
5.0%
Other values (8)1083823
22.7%
Common
ValueCountFrequency (%)
478388
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5262268
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i605435
11.5%
n590535
11.2%
a590535
11.2%
t478388
 
9.1%
478388
 
9.1%
M239194
 
4.5%
T239194
 
4.5%
u239194
 
4.5%
o239194
 
4.5%
e239194
 
4.5%
Other values (9)1323017
25.1%

study-local-timestamp
Categorical

HIGH CARDINALITY
UNIFORM

Distinct189637
Distinct (%)79.3%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2012-03-23 04:00:29.000
 
16
2012-03-27 04:00:29.000
 
15
2012-03-21 04:00:29.000
 
15
2012-03-27 16:00:29.000
 
14
2012-03-24 22:00:29.000
 
14
Other values (189632)
239120 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters5501462
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique154667 ?
Unique (%)64.7%

Sample

1st row2013-12-18 17:00:44.000
2nd row2013-12-19 05:00:45.000
3rd row2013-12-19 17:00:44.000
4th row2013-12-20 05:00:44.000
5th row2013-12-20 17:00:44.000

Common Values

ValueCountFrequency (%)
2012-03-23 04:00:29.00016
 
< 0.1%
2012-03-27 04:00:29.00015
 
< 0.1%
2012-03-21 04:00:29.00015
 
< 0.1%
2012-03-27 16:00:29.00014
 
< 0.1%
2012-03-24 22:00:29.00014
 
< 0.1%
2012-04-04 16:00:29.00014
 
< 0.1%
2012-04-05 04:00:29.00013
 
< 0.1%
2012-03-25 16:00:29.00013
 
< 0.1%
2012-03-21 10:00:29.00013
 
< 0.1%
2012-04-07 10:00:29.00013
 
< 0.1%
Other values (189627)239054
99.9%

Length

2021-11-11T17:20:35.512616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2012-04-101824
 
0.4%
2012-04-131823
 
0.4%
2012-04-091822
 
0.4%
2012-04-121820
 
0.4%
2012-04-111818
 
0.4%
2012-04-161815
 
0.4%
2012-04-181812
 
0.4%
2012-04-171800
 
0.4%
2012-04-141800
 
0.4%
2012-04-151784
 
0.4%
Other values (14985)460270
96.2%

Most occurring characters

ValueCountFrequency (%)
01729253
31.4%
1784294
14.3%
2597742
 
10.9%
-478388
 
8.7%
:478388
 
8.7%
3261239
 
4.7%
239194
 
4.3%
.239194
 
4.3%
4237951
 
4.3%
5137399
 
2.5%
Other values (4)318420
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4066298
73.9%
Other Punctuation717582
 
13.0%
Dash Punctuation478388
 
8.7%
Space Separator239194
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01729253
42.5%
1784294
19.3%
2597742
 
14.7%
3261239
 
6.4%
4237951
 
5.9%
5137399
 
3.4%
695350
 
2.3%
986980
 
2.1%
869331
 
1.7%
766759
 
1.6%
Other Punctuation
ValueCountFrequency (%)
:478388
66.7%
.239194
33.3%
Dash Punctuation
ValueCountFrequency (%)
-478388
100.0%
Space Separator
ValueCountFrequency (%)
239194
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5501462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01729253
31.4%
1784294
14.3%
2597742
 
10.9%
-478388
 
8.7%
:478388
 
8.7%
3261239
 
4.7%
239194
 
4.3%
.239194
 
4.3%
4237951
 
4.3%
5137399
 
2.5%
Other values (4)318420
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII5501462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01729253
31.4%
1784294
14.3%
2597742
 
10.9%
-478388
 
8.7%
:478388
 
8.7%
3261239
 
4.7%
239194
 
4.3%
.239194
 
4.3%
4237951
 
4.3%
5137399
 
2.5%
Other values (4)318420
 
5.8%

Interactions

2021-11-11T17:20:29.107563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:17.718021image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:19.080542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:20.440480image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:22.004136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:23.339667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:24.749077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:26.318570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:27.686142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:29.277055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:17.877634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:19.229224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:20.736740image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:22.148541image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:23.497874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:24.923647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:26.479241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:27.847006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:29.447552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:18.027420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:19.379606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:20.888452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:22.296791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:23.657259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:25.084307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:26.644173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:28.003005image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:29.619264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:18.176857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:19.529369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:21.040950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:22.438401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:23.807796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:25.236856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:26.795795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:28.154455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:29.782305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:18.325375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:19.673959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:21.191154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:22.582937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:23.955609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:25.387458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:26.937916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:28.307767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:29.943095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:18.478619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:19.828941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:21.344093image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:22.735416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:24.104928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:25.671371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:27.083640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:28.462174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:30.101061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:18.628823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:19.982126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:21.531702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:22.890345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:24.279486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:25.831517image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:27.231442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:28.617522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:30.251272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:18.774813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:20.130863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:21.680371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:23.036211image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:24.431552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:25.989640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:27.375240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:28.766419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:30.546567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:18.927532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:20.284133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:21.837386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:23.186659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:24.590271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:26.153696image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:27.530944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-11T17:20:28.929952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-11-11T17:20:35.605900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-11T17:20:35.927519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-11T17:20:36.115037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-11T17:20:36.297368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-11-11T17:20:36.447119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-11T17:20:30.832759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-11T17:20:31.424843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

event-idvisibletimestamplocation-longlocation-latexternal-temperaturegps:dopheight-rawsensor-typeindividual-taxon-canonical-nametag-local-identifierindividual-local-identifierstudy-nameutm-eastingutm-northingutm-zonestudy-timezonestudy-local-timestamp
09710294924True2013-12-19 00:00:44.000-111.99007357.212889-22.02.6350.59gpsCanis lupus1379113791ABoVE: Boutin Alberta Grey Wolf440200.2509826.341519e+0612NMountain Standard Time2013-12-18 17:00:44.000
19710294925True2013-12-19 12:00:45.000-112.04307857.211016-8.01.6335.05gpsCanis lupus1379113791ABoVE: Boutin Alberta Grey Wolf436995.7355236.341358e+0612NMountain Standard Time2013-12-19 05:00:45.000
29710294926True2013-12-20 00:00:44.000-112.10565557.260090-5.01.8409.08gpsCanis lupus1379113791ABoVE: Boutin Alberta Grey Wolf433304.7833256.346880e+0612NMountain Standard Time2013-12-19 17:00:44.000
39710294927True2013-12-20 12:00:44.000-112.10533657.261037-12.01.6414.98gpsCanis lupus1379113791ABoVE: Boutin Alberta Grey Wolf433325.7357596.346985e+0612NMountain Standard Time2013-12-20 05:00:44.000
49710294928True2013-12-21 00:00:44.000-112.10563057.260091-9.01.8416.13gpsCanis lupus1379113791ABoVE: Boutin Alberta Grey Wolf433306.2931026.346880e+0612NMountain Standard Time2013-12-20 17:00:44.000
59710294929True2013-12-21 12:00:44.000-112.10560557.260063-10.02.0418.13gpsCanis lupus1379113791ABoVE: Boutin Alberta Grey Wolf433307.7504796.346877e+0612NMountain Standard Time2013-12-21 05:00:44.000
69710294930True2013-12-22 00:00:44.000-112.11439657.256069-20.01.8416.40gpsCanis lupus1379113791ABoVE: Boutin Alberta Grey Wolf432770.2138126.346441e+0612NMountain Standard Time2013-12-21 17:00:44.000
79710294931True2013-12-22 12:00:45.000-112.00574157.191187-3.02.2404.36gpsCanis lupus1379113791ABoVE: Boutin Alberta Grey Wolf439218.3063626.339117e+0612NMountain Standard Time2013-12-22 05:00:45.000
89710294932True2013-12-23 00:01:44.000-111.97733057.258910-8.02.0355.62gpsCanis lupus1379113791ABoVE: Boutin Alberta Grey Wolf441043.3754806.346630e+0612NMountain Standard Time2013-12-22 17:01:44.000
99710294933True2013-12-23 12:00:38.000-111.95452757.323258-2.01.6359.67gpsCanis lupus1379113791ABoVE: Boutin Alberta Grey Wolf442519.3112656.353773e+0612NMountain Standard Time2013-12-23 05:00:38.000

Last rows

event-idvisibletimestamplocation-longlocation-latexternal-temperaturegps:dopheight-rawsensor-typeindividual-taxon-canonical-nametag-local-identifierindividual-local-identifierstudy-nameutm-eastingutm-northingutm-zonestudy-timezonestudy-local-timestamp
2391849710389521True2014-06-17 19:00:59.000-111.45324356.69243222.02.0255.76gpsCanis lupus3226332263BABoVE: Boutin Alberta Grey Wolf472239.8263706.283241e+0612NMountain Daylight Time2014-06-17 13:00:59.000
2391859710389522True2014-06-18 07:00:53.000-111.49065056.71201514.01.8344.00gpsCanis lupus3226332263BABoVE: Boutin Alberta Grey Wolf469964.3503486.285437e+0612NMountain Daylight Time2014-06-18 01:00:53.000
2391869710389523True2014-06-19 07:01:30.000-111.51744556.72173616.01.6354.67gpsCanis lupus3226332263BABoVE: Boutin Alberta Grey Wolf468332.2530786.286531e+0612NMountain Daylight Time2014-06-19 01:01:30.000
2391879710389524True2014-06-19 19:00:29.000-111.52563856.71283025.02.2350.22gpsCanis lupus3226332263BABoVE: Boutin Alberta Grey Wolf467823.2423376.285544e+0612NMountain Daylight Time2014-06-19 13:00:29.000
2391889710389525True2014-06-20 07:00:59.000-111.51698856.71912618.02.2354.06gpsCanis lupus3226332263BABoVE: Boutin Alberta Grey Wolf468358.0297516.286240e+0612NMountain Daylight Time2014-06-20 01:00:59.000
2391899710389526True2014-06-20 19:00:29.000-111.51913456.72006720.03.8353.46gpsCanis lupus3226332263BABoVE: Boutin Alberta Grey Wolf468227.4798066.286346e+0612NMountain Daylight Time2014-06-20 13:00:29.000
2391909710389527True2014-06-21 07:00:53.000-111.51753256.72186718.02.2357.11gpsCanis lupus3226332263BABoVE: Boutin Alberta Grey Wolf468327.0388306.286546e+0612NMountain Daylight Time2014-06-21 01:00:53.000
2391919710389528True2014-06-21 19:00:29.000-111.51499156.72248622.03.8346.52gpsCanis lupus3226332263BABoVE: Boutin Alberta Grey Wolf468483.0641316.286613e+0612NMountain Daylight Time2014-06-21 13:00:29.000
2391929710389529True2014-06-22 07:00:53.000-111.52656256.71338317.01.8361.25gpsCanis lupus3226332263BABoVE: Boutin Alberta Grey Wolf467767.1535056.285606e+0612NMountain Daylight Time2014-06-22 01:00:53.000
2391939710389530True2014-06-22 19:00:55.000-111.72961756.74903822.02.2460.30gpsCanis lupus3226332263BABoVE: Boutin Alberta Grey Wolf455379.8776646.289688e+0612NMountain Daylight Time2014-06-22 13:00:55.000